Senior ML Engineer: Productionized AI at Scale

Capital One; Plano, Northern (TX, KY); Hybrid; USD 179,000 - 205,000; Full time

Job summary

Capital One is seeking a Lead Machine Learning Engineer to join an Agile team focused on productionizing ML applications at scale. You will design, build, and deploy ML models and pipelines, collaborating with Product and Data Science peers to solve real-world business problems.

You will work across cloud platforms, ensure governance and responsible AI practices, and mentor teams while improving ML infrastructure, tests, and deployment automation.

Qualifications

• Bachelor’s Degree in a relevant field.
• 6+ years designing and building data‑intensive distributed solutions.
• 4+ years programming with Python, Scala, or Java.
• 2+ years building and optimizing ML systems.

Responsibilities

• Design and deliver ML models and components with Product and Data Science.
• Choose models, data, and features with ML techniques considerations.
• Write, test, and deploy ML code and models; automate tests.
• Work in a cross-functional Agile team to enable big data ML apps.
• Retrain and monitor models in production.
• Build cloud-based architectures to scale ML models.
• Create optimized data pipelines for ML.
• Implement CI/CD with test automation and monitoring.
• Ensure code quality and governance for responsible AI.
• Use Python, Scala, or Java.

Skills

ML modeling

Python

Scala

Java

Distributed computing

CI/CD

Cloud platforms

Education

Bachelor’s Degree

Master’s or PhD in CS/EE/Math

Tools

scikit-learn

PyTorch

Dask

Spark

TensorFlow

Job description

Capital One is seeking a Lead Machine Learning Engineer to join an Agile team focused on productionizing ML applications at scale. You will design, build, and deploy ML models and pipelines, collaborating with Product and Data Science peers to solve real-world business problems.

You will work across cloud platforms, ensure governance and responsible AI practices, and mentor teams while improving ML infrastructure, tests, and deployment automation.